Distributed ledger systems (DLSs), which can also be referred to as consensus networks, and/or blockchain networks, enable participating entities to securely and immutably store data. DLSs are commonly referred to as blockchain networks without referencing any particular user case. Examples of types of blockchain networks can include public blockchain networks, private blockchain networks, and consortium blockchain networks. A consortium blockchain network is provided for a select group of entities, which control the consensus process, and includes an access control layer.
Blockchain-based programs can be executed by a distributed computing platform such as an Ethereum. For example, the Ethereum virtual machine (EVM) provides the runtime environment for smart contracts in Ethereum. An Ethereum blockchain can be viewed as a transaction-based state machine. State data in an Ethereum can be assembled to a global shared-state referred to as a world state. The world state comprises a mapping between Ethereum account addresses and account states. The world state can be stored in data structures such as the Merkle Patricia tree (MPT).
Besides state data, blockchain networks can also store other types of data such as block data and index data. Block data can include block header and block body. The block header can include identity information of a particular block and the block body can include transactions that are confirmed with the block. When more and more transactions are entered into the blockchain, state data and block data can grow very large in size. In some DLSs, every node stores an entire copy of the blockchain, which can take large amount of storage space, even if some of the old block data or state data are not frequently visited. In some DLSs, a few shared nodes store the entire copy of the blockchain and share blockchain data with other blockchain nodes which can create “data inequality.”
Accordingly, it would be desirable to reduce the amount of data stored on the nodes in the DLS while maintaining data equality and data processing efficiency.